Book Image

Artificial Intelligence By Example - Second Edition

By : Denis Rothman
Book Image

Artificial Intelligence By Example - Second Edition

By: Denis Rothman

Overview of this book

AI has the potential to replicate humans in every field. Artificial Intelligence By Example, Second Edition serves as a starting point for you to understand how AI is built, with the help of intriguing and exciting examples. This book will make you an adaptive thinker and help you apply concepts to real-world scenarios. Using some of the most interesting AI examples, right from computer programs such as a simple chess engine to cognitive chatbots, you will learn how to tackle the machine you are competing with. You will study some of the most advanced machine learning models, understand how to apply AI to blockchain and Internet of Things (IoT), and develop emotional quotient in chatbots using neural networks such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs). This edition also has new examples for hybrid neural networks, combining reinforcement learning (RL) and deep learning (DL), chained algorithms, combining unsupervised learning with decision trees, random forests, combining DL and genetic algorithms, conversational user interfaces (CUI) for chatbots, neuromorphic computing, and quantum computing. By the end of this book, you will understand the fundamentals of AI and have worked through a number of examples that will help you develop your AI solutions.
Table of Contents (23 chapters)
21
Other Books You May Enjoy
22
Index

Summary

Emotional polysemy makes human relationships rich and excitingly unpredictable. However, chatbots remain machines and do not have the ability to manage wide ranges of possible interpretations of a user's phrases.

Present-day technology requires hard work to get a cognitive NPL CUI chatbot up and running. Small talk will make the conversation smoother. It goes beyond being a minor feature; courtesy and pleasant emotional reactions are what make a conversation go well.

We can reduce the limits of present-day technology by creating emotions in the users through a meaningful dialog that creates a warmer experience. Customer satisfaction constitutes the core of an efficient chatbot. One way to achieve this goal is to implement cognitive functions based on data logging. We saw that when a user answers "no" when we expect "yes," the chatbot needs to adapt, exactly the way we humans do.

Cognitive data logging can be achieved through...